Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.4 KiB
Average record size in memory30.1 B

Variable types

Numeric6
Categorical2

Alerts

Monthly_Revenue is highly overall correlated with Number_of_CustomersHigh correlation
Number_of_Customers is highly overall correlated with Monthly_RevenueHigh correlation
Menu_Price has unique values Unique
Marketing_Spend has unique values Unique
Average_Customer_Spending has unique values Unique
Monthly_Revenue has unique values Unique

Reproduction

Analysis started2024-12-10 17:07:44.269139
Analysis finished2024-12-10 17:07:56.155459
Duration11.89 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Number_of_Customers
Real number (ℝ)

High correlation 

Distinct90
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.271
Minimum10
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-12-10T17:07:56.377784image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13
Q130
median54
Q374
95-th percentile96
Maximum99
Range89
Interquartile range (IQR)44

Descriptive statistics

Standard deviation26.364914
Coefficient of variation (CV)0.49492057
Kurtosis-1.1788973
Mean53.271
Median Absolute Deviation (MAD)22
Skewness0.044938029
Sum53271
Variance695.10867
MonotonicityNot monotonic
2024-12-10T17:07:56.762094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 23
 
2.3%
99 21
 
2.1%
42 20
 
2.0%
10 18
 
1.8%
84 17
 
1.7%
33 17
 
1.7%
26 16
 
1.6%
60 15
 
1.5%
28 15
 
1.5%
95 15
 
1.5%
Other values (80) 823
82.3%
ValueCountFrequency (%)
10 18
1.8%
11 15
1.5%
12 14
1.4%
13 13
1.3%
14 13
1.3%
15 12
1.2%
16 8
0.8%
17 15
1.5%
18 13
1.3%
19 8
0.8%
ValueCountFrequency (%)
99 21
2.1%
98 14
1.4%
97 9
0.9%
96 10
1.0%
95 15
1.5%
94 8
 
0.8%
93 11
1.1%
92 5
 
0.5%
91 7
 
0.7%
90 7
 
0.7%

Menu_Price
Real number (ℝ)

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.21912
Minimum10.009501
Maximum49.97414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-12-10T17:07:57.123217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10.009501
5-th percentile12.190907
Q120.396827
median30.860614
Q339.843868
95-th percentile47.976615
Maximum49.97414
Range39.964638
Interquartile range (IQR)19.447041

Descriptive statistics

Standard deviation11.278759
Coefficient of variation (CV)0.37323254
Kurtosis-1.1237861
Mean30.21912
Median Absolute Deviation (MAD)9.5350227
Skewness-0.055329811
Sum30219.12
Variance127.21041
MonotonicityNot monotonic
2024-12-10T17:07:57.475077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.11763382 1
 
0.1%
33.51537323 1
 
0.1%
10.36121655 1
 
0.1%
33.53650665 1
 
0.1%
33.94855881 1
 
0.1%
25.86639023 1
 
0.1%
19.48111343 1
 
0.1%
19.38696098 1
 
0.1%
45.34838867 1
 
0.1%
27.68097496 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
10.0095005 1
0.1%
10.01329994 1
0.1%
10.04117966 1
0.1%
10.04480457 1
0.1%
10.04927349 1
0.1%
10.04946709 1
0.1%
10.09357548 1
0.1%
10.10336018 1
0.1%
10.13385487 1
0.1%
10.20328522 1
0.1%
ValueCountFrequency (%)
49.97414017 1
0.1%
49.9077034 1
0.1%
49.8534317 1
0.1%
49.8192215 1
0.1%
49.73347855 1
0.1%
49.68533325 1
0.1%
49.65901184 1
0.1%
49.61365128 1
0.1%
49.60593414 1
0.1%
49.56739807 1
0.1%

Marketing_Spend
Real number (ℝ)

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9587259
Minimum0.003767991
Maximum19.994276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-12-10T17:07:57.849570image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.003767991
5-th percentile0.88281242
Q14.690724
median10.092047
Q314.992436
95-th percentile18.962903
Maximum19.994276
Range19.990507
Interquartile range (IQR)10.301712

Descriptive statistics

Standard deviation5.8455868
Coefficient of variation (CV)0.58698139
Kurtosis-1.2355535
Mean9.9587259
Median Absolute Deviation (MAD)5.0939507
Skewness-0.026283424
Sum9958.7259
Variance34.170883
MonotonicityNot monotonic
2024-12-10T17:07:58.198994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.66379261 1
 
0.1%
10.3447876 1
 
0.1%
17.79304123 1
 
0.1%
0.2461940646 1
 
0.1%
7.401046753 1
 
0.1%
0.5484061837 1
 
0.1%
7.859484673 1
 
0.1%
10.51625156 1
 
0.1%
10.23362732 1
 
0.1%
3.312566996 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
0.003767990973 1
0.1%
0.03759767115 1
0.1%
0.04207686335 1
0.1%
0.04635095969 1
0.1%
0.05762503296 1
0.1%
0.05842065066 1
0.1%
0.06335697323 1
0.1%
0.07634267956 1
0.1%
0.1133162901 1
0.1%
0.121774964 1
0.1%
ValueCountFrequency (%)
19.99427605 1
0.1%
19.98932457 1
0.1%
19.94156265 1
0.1%
19.92245865 1
0.1%
19.89715958 1
0.1%
19.89297295 1
0.1%
19.84830856 1
0.1%
19.83485222 1
0.1%
19.83176804 1
0.1%
19.82526398 1
0.1%

Cuisine_Type
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Japanese
262 
American
256 
Mexican
250 
Italian
232 

Length

Max length8
Median length8
Mean length7.518
Min length7

Characters and Unicode

Total characters7518
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJapanese
2nd rowItalian
3rd rowJapanese
4th rowItalian
5th rowItalian

Common Values

ValueCountFrequency (%)
Japanese 262
26.2%
American 256
25.6%
Mexican 250
25.0%
Italian 232
23.2%

Length

2024-12-10T17:07:58.518160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T17:07:58.774017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
japanese 262
26.2%
american 256
25.6%
mexican 250
25.0%
italian 232
23.2%

Most occurring characters

ValueCountFrequency (%)
a 1494
19.9%
e 1030
13.7%
n 1000
13.3%
i 738
9.8%
c 506
 
6.7%
J 262
 
3.5%
p 262
 
3.5%
s 262
 
3.5%
A 256
 
3.4%
m 256
 
3.4%
Other values (6) 1452
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1494
19.9%
e 1030
13.7%
n 1000
13.3%
i 738
9.8%
c 506
 
6.7%
J 262
 
3.5%
p 262
 
3.5%
s 262
 
3.5%
A 256
 
3.4%
m 256
 
3.4%
Other values (6) 1452
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1494
19.9%
e 1030
13.7%
n 1000
13.3%
i 738
9.8%
c 506
 
6.7%
J 262
 
3.5%
p 262
 
3.5%
s 262
 
3.5%
A 256
 
3.4%
m 256
 
3.4%
Other values (6) 1452
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1494
19.9%
e 1030
13.7%
n 1000
13.3%
i 738
9.8%
c 506
 
6.7%
J 262
 
3.5%
p 262
 
3.5%
s 262
 
3.5%
A 256
 
3.4%
m 256
 
3.4%
Other values (6) 1452
19.3%

Average_Customer_Spending
Real number (ℝ)

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.477085
Minimum10.037177
Maximum49.900726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-12-10T17:07:59.069075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10.037177
5-th percentile12.095319
Q119.603041
median29.251366
Q339.553219
95-th percentile47.300492
Maximum49.900726
Range39.863548
Interquartile range (IQR)19.950178

Descriptive statistics

Standard deviation11.471685
Coefficient of variation (CV)0.38917299
Kurtosis-1.2372047
Mean29.477085
Median Absolute Deviation (MAD)9.9371767
Skewness0.025267767
Sum29477.085
Variance131.59956
MonotonicityNot monotonic
2024-12-10T17:07:59.416874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.23613358 1
 
0.1%
29.05289459 1
 
0.1%
25.76360703 1
 
0.1%
44.03807449 1
 
0.1%
23.8343544 1
 
0.1%
11.40860653 1
 
0.1%
39.84718704 1
 
0.1%
42.08153152 1
 
0.1%
16.42374802 1
 
0.1%
31.49361992 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
10.03717709 1
0.1%
10.06575012 1
0.1%
10.10832596 1
0.1%
10.15348625 1
0.1%
10.20558548 1
0.1%
10.27186203 1
0.1%
10.27258778 1
0.1%
10.28568363 1
0.1%
10.29567623 1
0.1%
10.31066608 1
0.1%
ValueCountFrequency (%)
49.90072632 1
0.1%
49.87236786 1
0.1%
49.82043839 1
0.1%
49.81896591 1
0.1%
49.75135803 1
0.1%
49.73060608 1
0.1%
49.71773911 1
0.1%
49.67556763 1
0.1%
49.46750259 1
0.1%
49.46252441 1
0.1%

Promotions
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
503 
1
497 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 503
50.3%
1 497
49.7%

Length

2024-12-10T17:07:59.983458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T17:08:00.192666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 503
50.3%
1 497
49.7%

Most occurring characters

ValueCountFrequency (%)
0 503
50.3%
1 497
49.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 503
50.3%
1 497
49.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 503
50.3%
1 497
49.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 503
50.3%
1 497
49.7%

Reviews
Real number (ℝ)

Distinct100
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.837
Minimum0
Maximum99
Zeros5
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-12-10T17:08:00.460111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q124
median50
Q376
95-th percentile95
Maximum99
Range99
Interquartile range (IQR)52

Descriptive statistics

Standard deviation29.226334
Coefficient of variation (CV)0.58643847
Kurtosis-1.2738841
Mean49.837
Median Absolute Deviation (MAD)26
Skewness0.018222622
Sum49837
Variance854.17861
MonotonicityNot monotonic
2024-12-10T17:08:00.850469image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 17
 
1.7%
92 17
 
1.7%
11 15
 
1.5%
30 15
 
1.5%
10 15
 
1.5%
38 15
 
1.5%
32 14
 
1.4%
22 14
 
1.4%
16 14
 
1.4%
81 14
 
1.4%
Other values (90) 850
85.0%
ValueCountFrequency (%)
0 5
 
0.5%
1 11
1.1%
2 5
 
0.5%
3 7
0.7%
4 8
0.8%
5 12
1.2%
6 10
1.0%
7 13
1.3%
8 9
0.9%
9 10
1.0%
ValueCountFrequency (%)
99 10
1.0%
98 12
1.2%
97 11
1.1%
96 12
1.2%
95 12
1.2%
94 4
 
0.4%
93 7
0.7%
92 17
1.7%
91 13
1.3%
90 10
1.0%

Monthly_Revenue
Real number (ℝ)

High correlation  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.72417
Minimum-28.977808
Maximum563.38135
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)0.5%
Memory size4.0 KiB
2024-12-10T17:08:01.154032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-28.977808
5-th percentile101.79945
Q1197.10364
median270.21397
Q3343.39578
95-th percentile438.10156
Maximum563.38135
Range592.35913
Interquartile range (IQR)146.29214

Descriptive statistics

Standard deviation103.98295
Coefficient of variation (CV)0.38695048
Kurtosis-0.28138438
Mean268.72417
Median Absolute Deviation (MAD)73.214722
Skewness-0.055728707
Sum268724.17
Variance10812.454
MonotonicityNot monotonic
2024-12-10T17:08:01.475005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350.9120483 1
 
0.1%
209.3143311 1
 
0.1%
45.96339417 1
 
0.1%
227.619751 1
 
0.1%
398.272583 1
 
0.1%
304.033905 1
 
0.1%
336.1401367 1
 
0.1%
27.11148834 1
 
0.1%
429.6379089 1
 
0.1%
177.4576721 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
-28.977808 1
0.1%
-28.92089081 1
0.1%
-27.61027527 1
0.1%
-25.45343018 1
0.1%
-7.627381802 1
0.1%
3.819308519 1
0.1%
27.11148834 1
0.1%
27.1511364 1
0.1%
27.35222054 1
0.1%
33.86572647 1
0.1%
ValueCountFrequency (%)
563.3813477 1
0.1%
542.4672852 1
0.1%
539.371582 1
0.1%
522.7666626 1
0.1%
518.3270264 1
0.1%
503.3291626 1
0.1%
496.6536865 1
0.1%
494.2657776 1
0.1%
492.3054504 1
0.1%
491.812439 1
0.1%

Interactions

2024-12-10T17:07:53.990054image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:44.759130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:46.441526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:47.964161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:49.967271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:52.299472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:54.215296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:45.064506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:46.686821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:48.216272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:50.333705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:52.533474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:54.481235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:45.314938image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:46.946623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:48.479247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:50.684291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:52.797271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:54.747113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:45.564785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:47.224354image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:48.813216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:51.077063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:53.247030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:54.998362image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:45.984580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:47.481861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:49.231390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:51.507234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:53.523418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:55.229458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:46.222161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:47.720794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:49.619652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:51.929502image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T17:07:53.762510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-10T17:08:01.686219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Average_Customer_SpendingCuisine_TypeMarketing_SpendMenu_PriceMonthly_RevenueNumber_of_CustomersPromotionsReviews
Average_Customer_Spending1.0000.014-0.0600.022-0.040-0.0140.0200.054
Cuisine_Type0.0141.0000.0000.0000.0000.0000.0000.000
Marketing_Spend-0.0600.0001.0000.0170.256-0.0070.000-0.030
Menu_Price0.0220.0000.0171.0000.2590.0350.1160.004
Monthly_Revenue-0.0400.0000.2560.2591.0000.7520.000-0.025
Number_of_Customers-0.0140.000-0.0070.0350.7521.0000.000-0.010
Promotions0.0200.0000.0000.1160.0000.0001.0000.000
Reviews0.0540.000-0.0300.004-0.025-0.0100.0001.000

Missing values

2024-12-10T17:07:55.568936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-10T17:07:55.937238image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Number_of_CustomersMenu_PriceMarketing_SpendCuisine_TypeAverage_Customer_SpendingPromotionsReviewsMonthly_Revenue
06143.11763412.663793Japanese36.236134045350.912048
12440.0200774.577892Italian17.952562036221.319092
28141.9814834.652911Japanese22.600420191326.529755
37043.0053064.416053Italian18.984098159348.190582
43017.4562003.475052Italian12.766143130185.009125
59219.42767013.114473American43.099949110399.867493
69635.35034216.960318American20.181622086496.653687
78446.31464414.486349Italian28.92110111417.158600
88422.64786315.841872American11.732611026352.148071
99733.5322698.095968Italian37.973579098272.793518
Number_of_CustomersMenu_PriceMarketing_SpendCuisine_TypeAverage_Customer_SpendingPromotionsReviewsMonthly_Revenue
9904334.77991110.967641Japanese17.314581051323.610352
9918521.89420117.585102Japanese19.296162030296.138153
9924427.21348813.511601American35.425282032341.419464
9931016.82659010.939265Mexican49.717739012139.180237
9944912.98519216.595263Italian41.108700117229.953125
9957341.30784212.122931Japanese19.033585140249.312027
9963120.6154965.822885Mexican17.040991057110.228767
9976917.1106554.141898Japanese44.649315055312.212555
9987337.6647223.046556Japanese27.767359023272.482208
9998134.72206917.989103Italian15.482112172379.973083